Manufacturers and suppliers are continuously looking for ways to monitor and improve processes. Customers are continuously monitoring the quality of goods they receive. Capability Analysis is an excellent method for determining and tracking whether or not a process is yielding acceptable results. A Capability Analysis compares the distribution of output from an in-control process to its specification limits to determine the consistency with which the specification limits can be met. Specifications or tolerances are the numerical limits within which a system is designed to operate. Customers, engineers, or management set the tolerances and/or outline other specifications for a product or process. Quality control is needed to monitor the finished product or the various processes leading to the finished product. Usually quality control leads to various charts and/or arrays of data that describe the process in great detail.
Capability Analysis requires that the data be statistically stable. If the data is unstable Capability Analysis can be used to track past performance or to obtain a snapshot of what is currently happening with a process. Future performance can be extrapolated, but there is no guarantee past instabilities will continue to have the same effects. Capability Analysis can also be used to demonstrate the extent of improvements made to a process. In addition, Capability Analysis can expose to what extent improvements to a process need to be made.
Capability Analysis involves a method of manipulating a plurality of data values in order to generate a single representative number instead of the various charts or arrays of data typically used to describe a process. From this single number, manufacturers, suppliers, and customers know immediately the risk of defects associated with a process. If the process capability is greater than unity, this would lead a person to believe the process is performing within tolerances. The larger the process capability the better the process is performing. In other words, the tolerance range is larger than the process range.
If the process capability is less than unity, for example if it is 0.8, then the level of risk is 80%; or, 80% of the time the process will yield a result that is within the tolerance range. The question that arises is, “Is 80% tolerance for a process an acceptable risk?” From this question, another decision can be made, whether the tolerance range for the process having an 80% level or risk needs to be widened or are there improvements that need to/can be made to the process to improve the level of risk? For example, the manufacturer of a bolt can monitor bolt production, and from a Capability Analysis, know immediately if the bolts being manufactured are within tolerances, and what percentage of the bolts manufactured will be out of tolerance, or, if the process needs improvement.
The problem with the Capability Analysis process is that in order to generate reliable process capability numbers, raw data is needed. While raw data may not be difficult to obtain, it is complicated to obtain raw data that is in a usable and uniform format. The main problem arises because, data is collected in diverse ways by different companies. Companies trying to manage process capability in a global environment find the data they receive from manufacturers and/or suppliers to be based in a myriad of software packages and/or that the output data is of a format that is not compatible amongst different companies or even different departments within a single company.
The equations used for process capability are standard. Regardless of the user, the same results should be obtained in spite of the data reporting methods. Accordingly, there is a need for a tool that provides for the generation of a global process capability factor without requiring data providers to come up with a common data model or format (i.e. common delimited format) that can be uploaded into a database for the end user to perform there own calculations. There is a need for a tool that can normalize raw data from different data providers and thereby allow for the generation of a process capability without requiring the manipulation of raw data from a data provider.